Concerning the problems that the existing traffic flow prediction models cannot accurately capture the spatio-temporal features of traffic data， and most models show good prediction performance in single-step prediction， and the prediction performance of models in multi-step prediction is not ideal， a Spatio-Temporal Traffic Flow Prediction Model based on Gated Convolution （GC-STTFPM） was proposed. Firstly， the Graph Convolution Network （GCN） combining with Gated Recurrent Unit （GRU） was used to capture the spatio-temporal features of traffic flow data. Then， a method of splicing and filtering the original data and spatio-temporal feature data by using gated convolution unit was proposed to verify the validity of spatio-temporal feature data. Finally， GRU was used as the decoder to make accurate and reliable prediction of future traffic flow. Experimental results on traffic dataset of Los Angeles Highway show that compared with Attention based Spatial-Temporal Graph Neural Network （ASTGNN） and Diffusion Convolutional Recurrent Neural Network （DCRNN） under single step prediction （5 min）， GC-STGCN model has the Mean Absolute Error （MAE） reduced by 5.9% and 9.9% respectively， and the Root Mean Square Error （RMSE） reduced by 1.7% and 5.8% respectively. At the same time， it is found that the prediction accuracy of this model is better than those of most existing benchmark models under three multi-step scales of 15， 30 and 60 min， demonstrating strong adaptability and robustness.

Because multiple kernel learning can avoid selection of kernel functions and parameters effectively， and graph clustering can fully mine complex structural information between samples， Multiple Kernel Graph Clustering （MKGC） has received widespread attention in recent years. However， the existing MKGC methods suffer from the following problems： graph learning technique complicates the model， the high rank of graph Laplacian matrix cannot ensure the learned affinity graph to contain accurate c connected components （block diagonal property）， and most of the methods ignore the high-order structural information among the candidate affinity graphs， making it difficult to fully utilize the multiple kernel information. To tackle these problems， a novel MKGC method was proposed. First， a new graph learning method based on capped simplex projection was proposed to directly project the kernel matrices onto graph simplex， which reduced the computational complexity. Meanwhile， a new block diagonal constraint was introduced to keep the accurate block diagonal property of the learned affinity graphs. Moreover， the low-rank tensor learning was introduced in capped simplex projection space to fully mine the high-order structural information of multiple candidate affinity graphs. Compared with the existing MKGC methods on multiple datasets， the proposed method has less computational cost and high stability， and has great advantages in Accuracy （ACC） and Normalized Mutual Information （NMI）.